{"title":"Non-fragile H∞ filter design for two-dimensional foransini-marchesini systems","authors":"Wu Xiao-Xue, Yang Tian-xing, Liu He-ping","doi":"10.1109/CCDC.2018.8407594","DOIUrl":null,"url":null,"abstract":"This paper addresses the problem of non-fragile H∞ filter design for two-dimensional systems. This filter has interval-type gain variation, which means that it cannot be fully performed. Firstly, by Finslers'lemma, the nonfragile filter design has been formulated as a robust convex optimization problem, which is solved by the classic vertex algorithm. It should be noticed that, for high dimensional systems, the number of LMIS involved is so large that it may be over the processing capacity of Matlab LMI Toolbox. To solve this difficulty, an efficient randomized algorithm is proposed. This algorithm can reduce the number of LMI conditions significantly. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.","PeriodicalId":409960,"journal":{"name":"2018 Chinese Control And Decision Conference (CCDC)","volume":"5 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Chinese Control And Decision Conference (CCDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2018.8407594","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper addresses the problem of non-fragile H∞ filter design for two-dimensional systems. This filter has interval-type gain variation, which means that it cannot be fully performed. Firstly, by Finslers'lemma, the nonfragile filter design has been formulated as a robust convex optimization problem, which is solved by the classic vertex algorithm. It should be noticed that, for high dimensional systems, the number of LMIS involved is so large that it may be over the processing capacity of Matlab LMI Toolbox. To solve this difficulty, an efficient randomized algorithm is proposed. This algorithm can reduce the number of LMI conditions significantly. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.